Neural Mechanisms of Speed-Accuracy Tradeoff

Heitz, Richard P.; Schall, Jeffrey D.

doi:10.1016/j.neuron.2012.08.030

Cited by 341 publications

(516 citation statements)

References 101 publications

(166 reference statements)

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“…This linking proposition cannot be taken for granted (44), and the current demonstration that mapping model parameters onto measures of individual accumulators depends on unobserved statistical properties of the ensemble in which these accumulators operate. However, the EPU model demonstrates the necessity of obtaining multielectrode recordings to assess correlations in neural accumulation rates.…”

Section: Discussionmentioning

confidence: 93%

Response times from ensembles of accumulators

Zandbelt

Purcell

Palmeri

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique ensemble model of RT, called e pluribus unum, which embodies the wellknown dictum "out of many, one." We used the e pluribus unum model to analyze the RTs produced by ensembles of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of ensembles of varying size. We found that predicted RT distributions are largely invariant to ensemble size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of ensemble RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high.is a core measure of human decisionmaking in experimental psychology (1). The random variation of RT across otherwise identical trials has been a puzzle since the mid-19th century. Since the 1960s, this variation of RT-measured in a wide range of perceptual, cognitive, and economic tasks (1-5)-has been explained through stochastic accumulator models. These models assume that a response is generated when evidence accumulates at a certain rate (v) over time to a threshold (θ) and that the stochastic variation of RTs arises primarily from random fluctuations in accumulation rates (Fig. 1A). Historically, these models were formulated and tested before data on the underlying neural processes were available.Subsequently, neurons exhibiting accumulating discharge rates in various RT tasks have been found in sensory, sensorimotor, and motor brain structures; in premotor circuits for limb and eye movements it is known that the neurons with accumulating activity are necessary and sufficient for initiating movements (6, 7). Movements are initiated when the trial-averaged accumulating spike rate of these neurons reaches a fixed activation level (6) (A RT ) like a threshold, and the distribution of RTs is accounted for by the stochastic variability in the rate of growth of neural activity toward A RT (Fig. 1B). This discovery inspired the conjecture that individual neurons instantiate the evidence accumulation process described by stochastic accumulator models (6). This conjecture has stimulated extensive research replicating the original observation and equating accumulator model parameters with measures of neural dynamics assessed by spike rates (8...

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Section: Discussionmentioning

confidence: 93%

Response times from ensembles of accumulators

Zandbelt

Purcell

Palmeri

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…Recent studies on simple perceptual decisions, though, suggest that, in addition to response criteria, the rate of evidence accumulation is also affected by time pressure (Heathcote & Love, 2012;Heitz & Schall, 2012;Ho et al, 2012;Rae et al, in press;Vandekerckhove et al, 2008). The present results of a more complex flanker task support this view as they demonstrate that modulations of drift rates and the duration of non-decision processes co-determine SAFs.…”

Section: Discussionmentioning

confidence: 99%

“…In line with previous studies on simple perceptual decisions, we expected a decrease of response selection criteria with increasing time pressure. Further, and more critically, a modulation of drift rates would indicate that time pressure also affects evidence accumulation (Heitz & Schall, 2012;Ho et al, 2012;Rae et al, in press). …”

Section: Present Studymentioning

confidence: 99%

“…Recent reports, however, suggest that the tradeoff account does not generally hold but that time pressure also affects the efficiency of evidence accumulation (cf., Heathcote & Love, 2012;Heitz & Schall, 2012;Starns, Ratcliff, & McKoon, 2012;Vandekerckhove, Tuerlinckx, & Lee, 2008). Indeed, a recent modeling study on several data sets revealed that an emphasis of speed over accuracy not only lowers response criteria but also comes with a reduction in the quality of perceptual processing (Rae, Heathcote, Donkin, Averell, & Brown, in press).…”

Section: Introductionmentioning

confidence: 99%

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Time pressure affects the efficiency of perceptual processing in decisions under conflict

Dambacher

Hübner

2014

Psychological Research

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The negative correlation between speed and accuracy in perceptual decision making is often explained as a tradeoff, where lowered decision boundaries under time pressure result in faster but more error-prone responses. Corresponding implementations in sequential sampling models confirmed the success of this account, which has led to the prevalent assumption that a second component of decision making, the efficiency of perceptual processing, is largely independent from temporal demands. To test the generality of this claim, we examined time pressure effects on decisions under conflict. Data from a flanker task were fit with a sequential sampling model that

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“…A recent paper by Rae, Heathcote, Donkin, Averell, and Brown (2014) provided both empirical and model-based evidence that manipulations of speed-and accuracy-emphasis may influence both caution and the rate of evidence accumulation (see also Heitz & Schall, 2012;Starns, Ratcliff, & McKoon, 2012;Vandekerckhove, Tuerlinckx, & Lee, 2008). 7 Intuitively, when emphasizing accuracy, participants may also try harder to do the task, in addition to collecting more evidence before responding.…”

Section: Alternative Analysis I: Caution and Easementioning

confidence: 99%

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Dutilh¹,

Annis²,

Brown³

et al. 2016

Preprint

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Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) nondecision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

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Neural Mechanisms of Speed-Accuracy Tradeoff

Cited by 341 publications

References 101 publications

Response times from ensembles of accumulators

Response times from ensembles of accumulators

Time pressure affects the efficiency of perceptual processing in decisions under conflict

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

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